Depth Is All You Need for Monocular 3D Detection

Depth Is All You Need for Monocular 3D Detection

5 October 2022

Jie Li

Vitor Campagnolo Guizilini

Adrien Gaidon

Papers citing "Depth Is All You Need for Monocular 3D Detection"

7 / 7 papers shown

Title
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models Johannes Meier Louis Inchingolo Oussema Dhaouadi Yan Xia Jacques Kaiser Daniel Cremers 58 0 0 17 Mar 2025
FlatFusion: Delving into Details of Sparse Transformer-based Camera-LiDAR Fusion for Autonomous Driving Yutao Zhu Xiaosong Jia Xinyu Yang Junchi Yan ViT 34 2 0 13 Aug 2024
Multi-View Attentive Contextualization for Multi-View 3D Object Detection Xianpeng Liu Ce Zheng Ming Qian Nan Xue C. L. P. Chen Zhebin Zhang Chen Li Tianfu Wu 38 2 0 20 May 2024
MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection Hou-I Liu Christine Wu Jen-Hao Cheng Wenhao Chai Shian-Yun Wang ... Jenq-Neng Hwang Hong-Han Shuai Wen-Huang Cheng Hong-Han Shuai Wen-Huang Cheng 42 2 0 07 Apr 2024
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection Zhuoling Li Z. Qu Yang Zhou Jianzhuang Liu Haoqian Wang Lihui Jiang 55 62 0 19 May 2022
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations Adel Ahmadyan Liangkai Zhang Jianing Wei Artsiom Ablavatski Matthias Grundmann 3DPC 148 174 0 18 Dec 2020
Deep Ordinal Regression Network for Monocular Depth Estimation Huan Fu Mingming Gong Chaohui Wang Kayhan Batmanghelich Dacheng Tao MDE 194 1,707 0 06 Jun 2018